Usage of activations

Activations can either be used through an Activation layer, or through the activation argument supported by all forward layers:

from keras.layers import Activation, Dense

model.add(Dense(64))
model.add(Activation('tanh'))

This is equivalent to:

model.add(Dense(64, activation='tanh'))

You can also pass an element-wise TensorFlow/Theano/CNTK function as an activation:

from keras import backend as K

model.add(Dense(64, activation=K.tanh))

Available activations

softmax

keras.activations.softmax(x, axis=-1)

Softmax activation function.

Arguments

  • x: Input tensor.
  • axis: Integer, axis along which the softmax normalization is applied.

Returns

Tensor, output of softmax transformation.

Raises

  • ValueError: In case dim(x) == 1.

elu

keras.activations.elu(x, alpha=1.0)

Exponential linear unit.

Arguments

  • x: Input tensor.
  • alpha: A scalar, slope of negative section.

Returns

The exponential linear activation: x if x > 0 and alpha * (exp(x)-1) if x < 0.

References

  • [Fast and Accurate Deep Network Learning by Exponential

Linear Units (ELUs)](https://arxiv.org/abs/1511.07289)


selu

keras.activations.selu(x)

Scaled Exponential Linear Unit (SELU).

SELU is equal to: scale * elu(x, alpha), where alpha and scale are pre-defined constants. The values of alpha and scale are chosen so that the mean and variance of the inputs are preserved between two consecutive layers as long as the weights are initialized correctly (see lecun_normal initialization) and the number of inputs is "large enough" (see references for more information).

Arguments

  • x: A tensor or variable to compute the activation function for.

Returns

The scaled exponential unit activation: scale * elu(x, alpha).

Note

  • To be used together with the initialization "lecun_normal".
  • To be used together with the dropout variant "AlphaDropout".

References


softplus

keras.activations.softplus(x)

Softplus activation function.

Arguments

  • x: Input tensor.

Returns

The softplus activation: log(exp(x) + 1).


softsign

keras.activations.softsign(x)

Softsign activation function.

Arguments

  • x: Input tensor.

Returns

The softplus activation: x / (abs(x) + 1).


relu

keras.activations.relu(x, alpha=0.0, max_value=None)

Rectified Linear Unit.

Arguments

  • x: Input tensor.
  • alpha: Slope of the negative part. Defaults to zero.
  • max_value: Maximum value for the output.

Returns

The (leaky) rectified linear unit activation: x if x > 0, alpha * x if x < 0. If max_value is defined, the result is truncated to this value.


tanh

keras.activations.tanh(x)

Hyperbolic tangent activation function.


sigmoid

keras.activations.sigmoid(x)

Sigmoid activation function.


hard_sigmoid

keras.activations.hard_sigmoid(x)

Hard sigmoid activation function.

Faster to compute than sigmoid activation.

Arguments

  • x: Input tensor.

Returns

Hard sigmoid activation:

  • 0 if x < -2.5
  • 1 if x > 2.5
  • 0.2 * x + 0.5 if -2.5 <= x <= 2.5.

linear

keras.activations.linear(x)

Linear (i.e. identity) activation function.

On "Advanced Activations"

Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. learnable activations, which maintain a state) are available as Advanced Activation layers, and can be found in the module keras.layers.advanced_activations. These include PReLU and LeakyReLU.